overwing-mcp

Create rule set

create_rule_set

Create a custom rule set with 1 to 25 rules to define pass/fail and review criteria for automated text scoring.

Instructions

Create a custom rule set with 1 to 25 rules. Each rule is a choice (pick one option), score (position on an ordered scale), or noul (yes/no) question with a fail condition, optional review threshold, and weight.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "name": "<name>",
      "slug": "<slug>",
      "rules": "<rules>"
    },
    "name": "create_rule_set"
  }
}

Input Schema

Name	Required	Description	Default
`name`	Yes
`slug`	Yes
`rules`	Yes
`description`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.3.0Sep 23, 2026

TDQS

B3.2/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the rule count limit (1-25) and rule structure, but doesn't disclose side effects (e.g., whether creation overwrites an existing slug, whether it requires authentication, or what happens on validation failure). For a mutation tool with zero annotation coverage, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, dense sentence that front-loads the core purpose and then packs the key rule structure details. Every clause earns its place, though it could be slightly more structured with a second sentence for parameter semantics.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a creation tool with no output schema and no annotations, the description covers the rule structure well but omits important context: what the response looks like, whether slug must be unique, and any prerequisites (e.g., authentication). The sibling tools suggest a broader evaluation workflow, but the description doesn't situate this tool within that workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It explains the meaning of 'rules' (each rule is a choice/score/noul question with fail condition, review threshold, weight) and the count constraint, which adds value beyond the raw schema. However, it doesn't explain the 'name' and 'slug' parameters' semantics (e.g., slug format, uniqueness), leaving some gaps.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Create') and resource ('custom rule set'), and enumerates the three question types (choice, score, noul) with their key attributes. It distinguishes itself from sibling tools like evaluate and list_rule_sets by clearly being a creation tool, though it doesn't explicitly name any sibling.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context by describing what a rule set is and its constraints (1-25 rules, question types), but it doesn't explicitly state when to use this tool versus alternatives like evaluate or get_rule_set. The context is clear enough for an agent to infer this is for creating, not evaluating or listing, but no explicit when/when-not guidance is given.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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